Vision Transformer (ViT-7/4).
Fig. REF  (left) compares vision transformers trained with different data augmentation strategies. Again, NFM improves the robustness of the models while achieving state-of-the-art accuracy when evaluated on clean data. However, mixup and manifold mixup do not boost the robustness. Further, Fig. REF  (right) shows that that the vision transformer is less sensitive to salt and pepper perturbations as compared to the ResNet model. These results are consistent with the high robustness properties of transformers recently reported in [1]}, [2]}. Table REF  provides additional results for different \(\alpha \)  values.
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